Optimal speed control of a multiple-mass train for minimum energy consumption using ant colony and genetic algorithms

Today, rail transport systems are widely used in the world. Because of the high consumption of energy in these systems, finding ways to optimize their energy consumption is important. One of the best ways to save more energy and prevent the losses of rail transportation is using the optimal speed profile. In this article, intelligent algorithms, involving ant colony optimization for continuous domain ( ACO R ) and genetic algorithm, are applied to the energy efficiency problem of electrical trains for various track gradients and curvatures. With proper determination of switching points in the moving strategies such as acceleration, cruising, and coasting, the optimal speed profile for the safe journey of trains will be obtained. For the simulation, real data from rail tracks of Tehran metro have been incorporated.

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